The rapid increase in electronic transactions has led to the availability of massive volumes of web transaction data. Web transaction data generally refers to transaction data residing on World Wide Web (WWW) servers. WWW generally refers to all the resources and users on the Internet (a worldwide system of computer networks) using the Hypertext Transfer Protocol (HTTP).
Business research efforts have always focused on how to turn raw web transaction data into usable information. For example, by exploring web data access behavior, business system analysts may be able to find and retain their most valuable users and evolve their best service strategies.
A web transaction typically starts with a user clicking on a web page to request a web service or information. The request is passed through one or more web servers which respond to the user accordingly with the median server response being measured in milliseconds. In order to provide faster service, web system analysts need to analyze web transaction data and try to balance the workload among their web servers to prevent network bottlenecks. When the web transaction data set is fairly large, one problem faced by system analysts is how to visually analyze and correlate the performance of millions of web transactions.
A common technique for visualizing web access is a two-dimensional scatter plot. The scatter plot technique positions pairs of web clients and server response time on separate axes to visualize their relationships. However, visualizing massive web transaction data sets using a scatter plot is too restrictive. The scatter plot is typically capable of only showing a maximum of 10-20 data items without overlapping. When the number of data items is in the thousands, the scatter plot display becomes too cluttered. In such case, the scatter plot may exhibit too much overlapping which occurs due to high-density data, as generally shown in FIG. 7. Furthermore, scatter plots do not support user interactions such as zoom in/out, drill-down, etc. Scatter plots are not scalable when fairly large volumes of web transaction data are involved. Moreover, no real-time visual filtering is possible with scatter plots, i.e. data pre-processing is always needed when analyzing massive volumes of web transaction data.